We are studying the effect of an inhibitor of the cGAS-STING signaling pathway H151 on T-ALL model cell line Jurkat.
The biological experiment revealed that H151 causes cell death, so the pathway is important for survival of T-ALL.
We want to explore the differential expressed genes between normal condition and by inhibiting the pathway. Thus we are going to perform a differential expression analysis (DEA) followed by a pathway enrichment analysis (PEA)
Following a basic RNA-seq pipeline analysis
First let’s view the distribution of the different bio types we have in our data :
In the downstream analysis (DEA), we’ll
be focusing on the top 2 biotypes (protein_coding and
lncRNA). Additional filtering will be applied :
MaxCount_threshold = 20 (At least 1
sample must have a read count over that value)CpmCount_threshold = 0.5 (Count per
million reads threshold)MinSample = 3 (Samples that should
pass the cpm threshold)Now to understand the global gene expression landscape and to assess the quality control of our data, we need to perform a dimentionality reduction analysis : Principal component analysis (PCA)
The PCA :
So we need to perform some data transformation first on
raw counts.
We’ll be using a Variance Stabilizing
Transformation (VST) from the DESeq2 package. And
This will :
DESeq2 computes size
factors using a median-of-ratios method)Now let’s dive into gene expression.
dds <- pre_process_results$DESeqData
dds$Condition <- relevel(dds$Condition, ref = "Jurkat_ct") ## set the control
dds <- DESeq(dds) Let’s get a quick overview of the distribution of the DEGs
Let’s draw a heatmap of the top 20up and 20down genes
DEGs list overview
1- protein-coding genes
## [1] "CXCR3" "APLN" "H2BC21" "RGS6"
## [5] "BHLHE40" "COL6A3" "VGF" "SLAMF8"
## [9] "KANK4" "WNT8B" "TMEM240" "MSLNL"
## [13] "TMEM163" "NANOS1" "RPRML" "CCR8"
## [17] "KIT" "TMEM269" "TERT" "CLEC12A"
## [21] "RNASEH2A" "DTL" "UNG" "MCM4"
## [25] "TCHH" "LVRN" "GPR146" "ENSG00000289128"
## [29] "CXXC4" "REEP1" "LRRC10B" "DDIT4"
## [33] "H4C9" "TINCR" "DDT" "SCLY"
## [37] "HERC3" "PTPN6" "SLC37A2" "TRARG1"
## [41] "DENND2D" "TMEM255B" "PCNA" "GET1"
## [45] "MYT1L" "UHRF1" "C1S" "ELFN2"
## [49] "FAM81A" "SASH3" "JAKMIP1" "TWIST1"
## [53] "ABTB3" "ADGRL2" "MYO18B" "ENSG00000102409"
## [57] "CSPG5" "LNP1" "GNA15" "MCM2"
## [61] "P2RX6" "FA2H" "MCM3" "OPN3"
## [65] "SLBP" "NECAB3" "EID2B" "PDE4A"
## [69] "MCM7" "IGLL1" "NOTCH1" "PPM1H"
## [73] "PDXP" "JMJD7" "DTX1" "CALML4"
## [77] "FEN1" "RMI2" "KRT72" "HK2"
## [81] "GNMT" "TCF7" "PIGW" "ATP2B2"
## [85] "IMPDH2" "ZNF704" "H3C10" "OXTR"
## [89] "PANO1" "FBXL22" "TTC7A" "NT5DC2"
## [93] "POLG" "NPIPB13" "ANKRD37" "NUP210"
## [97] "WT1" "ZMYND19" "RHPN2" "CXXC5"
## [101] "NCBP2AS2" "NPIPB11" "GNG4" "NOTCH2NLB"
## [105] "IVNS1ABP"
2- lncRNA genes
## [1] "MIR210HG" "ENSG00000288930" "RANBP3-DT" "IRAIN"
## [5] "ENSG00000196465" "ENSG00000181577" "ENSG00000146223" "LINC00484"
## [9] "ENSG00000125651" "ENSG00000211454" "ENSG00000236204" "ENSG00000213139"
## [13] "LINC01963" "ENSG00000170571" "ENSG00000086300" "ENSG00000136840"
## [17] "ENSG00000290124" "ENSG00000269968" "ENSG00000230148" "ILF3-DT"
## [21] "ENSG00000173914" "ENSG00000171357" "ENSG00000138614" "PKD1P6-NPIPP1"
## [25] "ENSG00000261226" "ENSG00000183979" "ENSG00000165028" "ENSG00000182197"
## [29] "ASH1L-AS1" "ENSG00000205903" "TOLLIP-DT" "MIF4GD-DT"
## [33] "ENSG00000272356" "ZNF674-AS1" "ENSG00000198034" "ENSG00000143971"
## [37] "YTHDF3-DT" "ENSG00000287362" "ENSG00000277767" "FAM30A"
## [41] "ENSG00000233178" "ENSG00000276390" "ENSG00000187164" "ENSG00000142556"
## [45] "ENSG00000124370" "ENSG00000167863" "ENSG00000224066" "ENSG00000151093"
## [49] "ENSG00000165475" "C16orf95-DT" "ENSG00000289985" "ZNF346-IT1"
## [53] "ENSG00000259736" "ENSG00000288586" "ENSG00000278611" "ENSG00000140092"
## [57] "ENSG00000231527" "FOXD2-AS1" "LINC00641" "LRRC8D-DT"
## [61] "NIPBL-DT" "ENSG00000138738" "ENSG00000251247" "ENSG00000183891"
## [65] "ENSG00000163935" "PRKCZ-AS1" "SLFNL1-AS1" "ENSG00000137941"
## [69] "SLC16A4-AS1" "ZNF710-AS1" "MAD2L1-DT" "ENSG00000260442"
## [73] "ENSG00000244184" "GJD3-AS1" "TBC1D22A-DT" "ENSG00000157800"
## [77] "ENSG00000126107" "MHENCR" "ENSG00000197548" "ENSG00000133794"
## [81] "SNHG10" "C9orf163" "ENSG00000175040" "ENSG00000143740"
## [85] "ENSG00000105808" "RASSF1-AS1" "ENSG00000178982" "ENSG00000185621"
## [89] "PDK1-AS1" "ENSG00000291056" "SUGT1-DT" "ENSG00000124140"
## [93] "LINC01311" "ENSG00000285437" "ENSG00000100003" "NKILA"
## [97] "ENSG00000243479" "ENSG00000100321" "ENSG00000287104" "ENSG00000158555"
## [101] "ENSG00000153936" "MCPH1-DT" "RNF139-DT" "ENSG00000173218"
## [105] "ENSG00000155366" "ENSG00000267757" "ENSG00000055950" "ENSG00000162413"
## [109] "MCF2L-AS1" "ENSG00000246308" "ENSG00000065413" "ENSG00000113595"
## [113] "LINC01284" "ENSG00000139266" "ENSG00000153561" "ENSG00000118242"
## [117] "GABPB1-IT1" "CIRBP-AS1" "ENSG00000267688" "ENSG00000114019"
## [121] "LINC01971" "ENSG00000068394" "ENSG00000185885" "PARTICL"
## [125] "LINC00235" "MIR193BHG" "ST3GAL1-DT" "ENSG00000196810"
## [129] "ENSG00000286548" "ENSG00000006194" "PAXIP1-DT" "ENSG00000106336"
## [133] "LINC00528" "CNN3-DT" "ENSG00000198353" "ENSG00000108479"
## [137] "PRKCH-AS1" "ENSG00000221823" "ENSG00000165915" "ENSG00000160867"
## [141] "LINC02019" "YEATS2-AS1" "STAM-DT" "ENSG00000104219"
## [145] "LINC00304" "RORB-AS1" "ENSG00000149823" "LINC01422"
## [149] "ENSG00000177556" "DNAH10OS" "LINC01389" "ENSG00000101336"
## [153] "ENSG00000276853" "ENSG00000213047" "LINC02918" "DANCR"
1- protein-coding genes
## [1] "CEP126" "ZNF836" "SLC26A11" "CLEC18B" "PCDHGA6" "SLC8A2"
## [7] "PLCD1" "RNFT1" "RELB" "ZBTB7B" "ANKRD31" "PCYT1B"
## [13] "MACO1" "NQO2" "NIPAL1" "PLTP" "ZNF585B" "NPIPA5"
## [19] "STPG1" "DMGDH" "CFAP69" "ZNF329" "APOBEC3G" "CLIC4"
## [25] "C2orf50" "SERPINI1" "B3GNT5" "PLEKHA6" "GDPD1" "MAP1A"
## [31] "GABRB2" "DUSP16" "CCT6B" "GARIN1A" "ACTRT3" "ELL2"
## [37] "BCHE" "SHF" "TCP11L2" "ARMH4" "TMEM79" "TSPAN10"
## [43] "PEX5L" "MYO5B" "TBC1D30" "FGF22" "RGPD3" "ANG"
## [49] "SPRY3" "IFI35" "DOK4" "IFRD1" "CASP9" "CNGA1"
## [55] "SPTB" "ZNF610" "C3AR1" "PCDHGA10" "ERRFI1" "FRRS1"
## [61] "TNS2" "MPZL3" "TSNAXIP1" "EGFL7" "SCG2" "SLC16A9"
## [67] "DENND2C" "RSPH4A" "SLC17A7" "FZD7" "MCTP1" "HOXB6"
## [73] "WNK4" "RAB39B" "NUDT13" "LRP12" "CYP2J2" "WDR31"
## [79] "CTSG" "ID1" "SLCO3A1" "SYNE4" "CD84" "SLC38A3"
## [85] "FAM219A" "CYSRT1" "SERTAD1" "DNAAF8" "LAT2" "CA13"
## [91] "DNAJC3" "TNN" "ERBB2" "CAPS" "JAM2" "ULBP2"
## [97] "RXRA" "FLVCR2" "PILRA" "C11orf65" "SPNS3" "ICOS"
## [103] "CBS" "EVI2A" "RTN2" "ADAM8" "IL20RB" "BEND6"
## [109] "FTH1" "TMEM151A" "TMEM267" "GCNT4" "PEX11G" "C19orf38"
## [115] "DNAI7" "DDRGK1" "CCDC184" "P2RY10" "TMBIM1" "OASL"
## [121] "FOSB" "DBNDD1" "CCDC69" "NECTIN3" "JAML" "IRAK2"
## [127] "EFNB2" "TAGLN" "BVES" "SYNGR4" "CACNG8" "CCNB3"
## [133] "FMO4" "PEAR1" "SLC43A1" "MIA2" "RAB17" "CDS1"
## [139] "EVA1B" "TPST1" "CYB5R1" "C14orf39" "NES" "APOL1"
## [145] "SSC4D" "TXNRD1" "C22orf23" "METTL27" "EHD2" "PDGFRL"
## [151] "MAML2" "ZNF235" "GAB2" "CGRRF1" "DAPP1" "LCA5L"
## [157] "DNAJB9" "IRAG2" "CCDC65" "GPT2" "SLC9A9" "WIPI1"
## [163] "EPHX1" "MN1" "SEL1L3" "NUTM2E" "RBP5" "BBC3"
## [169] "EPS8" "NEK5" "ASS1" "ARHGAP29" "RGS17" "EIF4EBP1"
## [175] "ABHD3" "TXK" "EID3" "CTSO" "PGGHG" "ACTN2"
## [181] "BMP10" "KIF5C" "CYP1A1" "MFAP3L" "TRIM22" "LAMC3"
## [187] "ZFP2" "ZNF582" "PCLO" "PLEKHH3" "EAF2" "NAGS"
## [193] "ATF3" "HSPA5" "HABP2" "GCLM" "HERPUD1" "IL23A"
## [199] "TIAM2" "TM6SF1" "DLL1" "ADGRB1" "SLC2A12" "TLR3"
## [205] "TLR5" "DGKG" "ASNS" "NKX2-2" "FAM43A" "IL15RA"
## [211] "PTGER3" "CDKN1A" "C7orf31" "FAAH2" "TFE3" "ABHD4"
## [217] "SLC48A1" "IRF7" "HRH1" "AMIGO2" "AARD" "CILP2"
## [223] "PCK2" "SMPDL3B" "DMRTA2" "TNFSF9" "UBXN8" "PSAT1"
## [229] "NPAS1" "CECR2" "EFNA1" "ENTPD1" "PLCL1" "CD55"
## [235] "PRG4" "SV2B" "CREB3L3" "REPS2" "ETFBKMT" "ISG20"
## [241] "PPFIBP2" "INCA1" "TMOD1" "ARRDC3" "SYT1" "SLC1A5"
## [247] "SESN2" "YPEL4" "ULBP1" "HRK" "PPP2R3A" "FRMD3"
## [253] "TCAF2" "NOS1AP" "DRC3" "RBM11" "SNAI1" "POF1B"
## [259] "SMTNL2" "NCF2" "ZNF880" "NCAM1" "TMEM200A" "LY9"
## [265] "IQUB" "MT1F" "DRAM1" "SMOX" "CLIP2" "CEBPB"
## [271] "TRPC5OS" "AMOT" "DAB1" "RASD1" "MET" "SGIP1"
## [277] "PHOSPHO1" "GABARAPL1" "NECTIN2" "IL7R" "CCER2" "FUT1"
## [283] "WNT10A" "MYH14" "NIBAN1" "TMEM217" "PLAU" "MYOM2"
## [289] "DEPTOR" "PMEL" "FLRT1" "SMAD7" "ADGRL3" "PRKG2"
## [295] "GLS2" "LDB3" "PCDHB14" "MPZ" "TMEM156" "TRAF3IP2"
## [301] "TLL1" "MFAP4" "MAFB" "SLC30A1" "TEX14" "KANK3"
## [307] "LPIN3" "THEMIS2" "SSC5D" "SHISA2" "FMN1" "SCN3A"
## [313] "LACC1" "LPAR4" "NDUFA4L2" "TMEM74B" "XKRX" "CDC20B"
## [319] "HYDIN" "PCDHGB7" "TBL1X" "ST8SIA6" "SCN3B" "RAD9B"
## [325] "ADGRE1" "STC2" "TLR1" "ITGB5" "JDP2" "NR2F2"
## [331] "CRPPA" "ERBB3" "CDNF" "ANXA3" "GBP2" "CALCRL"
## [337] "TNFRSF11A" "ZNF703" "CHRNB4" "ADM2" "HSPA12B" "TRPC1"
## [343] "LMX1B" "PTGES" "REXO5" "CATSPERG" "PCDHGB6" "CREB5"
## [349] "TEX19" "TGFBR3" "SLC45A1" "COLGALT2" "SYT11" "SH2D6"
## [355] "PCDH12" "RPS6KA2" "CST7" "DUSP8" "TREML2" "WDR93"
## [361] "PIFO" "CDH15" "TMIE" "HSPA1B" "CEACAM1" "SLC16A6"
## [367] "CCDC148" "ZNF35" "ZFPM2" "CFTR" "PCDH19" "SP140"
## [373] "GPAT3" "PPP1R15A" "FBXO39" "CXCL3" "TRAT1" "SLC3A2"
## [379] "SORBS1" "MTTP" "KIF17" "ADTRP" "ZBTB46" "RGS16"
## [385] "PIWIL4" "FLRT3" "JAKMIP2" "TMEM140" "SPTA1" "CSTA"
## [391] "TC2N" "SCN4B" "PLAC1" "JAKMIP3" "FOXA3" "PRDM1"
## [397] "HTR2B" "F2RL2" "NQO1" "SOX6" "PATJ" "CHAC1"
## [403] "GPR18" "FAM166B" "CRB3" "MKX" "TLR6" "SCN4A"
## [409] "CLCA1" "LY96" "IL31RA" "NYAP1" "DPP4" "TRPM6"
## [415] "CCPG1" "AKR1C3" "TRPS1" "CEACAM21" "IL12A" "ID2"
## [421] "OLAH" "MAP3K8" "GLIPR1" "TRIB3" "STYK1" "MATN4"
## [427] "BEST1" "CCDC110" "TSC22D3" "KCNN3" "FAM133A" "CLU"
## [433] "PDE11A" "ADRA1A" "JUN" "CHRNA6" "DDIT3" "LRRC2"
## [439] "HEPH" "ILDR1" "SATL1" "GPR132" "FOSL1" "XKR3"
## [445] "SLC6A9" "INPP5J" "GPX2" "AKR1C8" "CRIM1" "UEVLD"
## [451] "SLC7A11" "TSPAN19" "CYP19A1" "PANX2" "CD80" "BMP6"
## [457] "AKR1C2" "OSGIN1" "MMP8" "EGF" "HRG" "GDAP1L1"
## [463] "ABCC3" "UCP1" "HSPA6" "RHOB" "RASSF6" "HMOX1"
## [469] "C4orf17"
2- lncRNA genes
## [1] "RAP2C-AS1" "DDX19A-DT" "SP2-DT" "ENSG00000204482"
## [5] "LINC01058" "LINC00652" "LINC01134" "LINC01215"
## [9] "LINC-PINT" "USP27X-DT" "ENSG00000289958" "RPAP3-DT"
## [13] "ENSG00000138061" "ENSG00000272438" "ERCC6L2-AS1" "PCAT1"
## [17] "ENSG00000102245" "SNRPA1-DT" "ACTR3-AS1" "ENSG00000261173"
## [21] "LINC01115" "CPEB1-AS1" "ENSG00000177788" "PRDM8-AS1"
## [25] "MIR4280HG" "LINC00662" "ENSG00000291010" "TWF2-DT"
## [29] "RORA-AS1" "ENSG00000178896" "COPB2-DT" "ZNF225-AS1"
## [33] "ENSG00000197024" "LNCRNA-IUR" "LINC01465" "LINC00853"
## [37] "MIR4435-2HG" "ENSG00000105698" "ENSG00000213588" "ENSG00000213465"
## [41] "FAM174A-DT" "ZNF451-AS1" "LINC02709" "HDAC2-AS2"
## [45] "ENSG00000260874" "ENSG00000125775" "PCOTH" "BAZ2B-AS1"
## [49] "BTG1-DT" "ENSG00000228436" "SDCBP2-AS1" "ENSG00000140104"
## [53] "ENSG00000108599" "ENSG00000275793" "TMEM254-AS1" "ENSG00000285706"
## [57] "ENSG00000288996" "HULC" "ALG1L9P" "ZFAND2A-DT"
## [61] "LINC01825" "RPL37A-DT" "SAMD12-AS1" "ENSG00000162543"
## [65] "LINC00877" "LYPLAL1-DT" "MKLN1-AS" "BTG2-DT"
## [69] "ENSG00000188761" "ENSG00000283141" "ECE1-AS1" "DLGAP1-AS2"
## [73] "WEE2-AS1" "ENSG00000021574" "OXR1-AS1" "LINC00680"
## [77] "ENSG00000289183" "ENSG00000273183" "SPOPL-DT" "LINC02901"
## [81] "LGALS8-AS1" "FBXO38-DT" "LINC01694" "ENSG00000102543"
## [85] "LINC00659" "ENSG00000204356" "ENSG00000198752" "MIR22HG"
## [89] "LINC00511" "ENSG00000243811" "LINC00239" "LINC00882"
## [93] "ENSG00000214770" "ENSG00000291136" "SLC12A5-AS1" "ENSG00000120071"
## [97] "MAGOH-DT" "MIAT" "YWHAH-AS1" "KRT10-AS1"
## [101] "ENO1-AS1" "LINC00685" "LINC01277" "ENSG00000291027"
## [105] "KDSR-DT" "ENSG00000006634" "ENSG00000160703" "TTTY14"
## [109] "LINC02265" "ENSG00000226029" "CEBPB-AS1" "ENSG00000159496"
## [113] "FKTN-AS1" "LINC01307" "ZNF516-AS1" "ENSG00000139800"
## [117] "LACTB2-AS1" "ENSG00000289115" "LINC02377" "ZBED3-AS1"
## [121] "ENSG00000214046" "ENSG00000115355" "ENSG00000161016" "LINC00029"
## [125] "ENSG00000119684" "RPL26L1-AS1" "PRRT3-AS1" "CBR3-AS1"
## [129] "JDP2-AS1" "ENSG00000172071" "ENSG00000110063" "PRMT5-DT"
## [133] "ENSG00000124172" "ENSG00000205643" "ENSG00000287808" "ENSG00000163794"
## [137] "GAS1RR" "ENSG00000149451" "ENSG00000284308" "ENSG00000135436"
## [141] "VLDLR-AS1" "ENSG00000227500" "ENSG00000180611" "LINC00992"
## [145] "UBQLN1-AS1" "ENSG00000157992" "LINC02341" "LINC02018"
## [149] "NQO1-DT" "LINC00648" "ENSG00000158406" "ENSG00000198888"
## [153] "ECI2-DT" "ERICH2-DT" "ENSG00000105516" "MDN1-AS1"
## [157] "ENSG00000254510" "LINC02321" "HIPK1-AS1" "ENSG00000196132"
## [161] "ENSG00000188739" "PRPF19-DT" "ENSG00000171735" "PCDH10-DT"
## [165] "ENSG00000219200" "ENSG00000132005" "LINC00632" "ROCK1P1"
## [169] "SLC26A4-AS1" "ENSG00000263528" "LUCAT1" "ENSG00000272010"
## [173] "ENSG00000259905" "ENSG00000163154" "LINC02561" "STX5-DT"
## [177] "LINC02539" "L3MBTL2-AS1" "ENSG00000290683" "LINC00365"
## [181] "NMRAL2P"
Now inspecting the gene biotypes
Let’s inspect the top up and down genes deeper !
NOTE:
For the downstream analysis, only protein_coding genes will
be kept to ensure the significance of the resulting terms!
We will be using first all the protein_coding DEGs (574)
then only Down-regulated one (469)
Let’s inspect the pathways enriched with all the DEGs (up & down)
that are protein_coding
The top 40 most significant pathways enriched with these DEGs are
## [1] "leukocyte activation"
## [2] "lymphocyte activation"
## [3] "multicellular organismal-level homeostasis"
## [4] "regulation of cell activation"
## [5] "regulation of leukocyte activation"
## [6] "cell-cell adhesion"
## [7] "regulation of leukocyte differentiation"
## [8] "T cell activation"
## [9] "myeloid leukocyte activation"
## [10] "blood vessel morphogenesis"
## [11] "regulation of hemopoiesis"
## [12] "response to endoplasmic reticulum stress"
## [13] "vasculature development"
## [14] "response to lipid"
## [15] "negative regulation of immune system process"
## [16] "regulation of T cell mediated immunity"
## [17] "response to ketone"
## [18] "leukocyte differentiation"
## [19] "integrated stress response signaling"
## [20] "epithelial cell proliferation"
## [21] "blood vessel development"
## [22] "circulatory system process"
## [23] "response to organic cyclic compound"
## [24] "mononuclear cell differentiation"
## [25] "lymphocyte differentiation"
## [26] "endothelial cell migration"
## [27] "response to topologically incorrect protein"
## [28] "angiogenesis"
## [29] "regulation of lymphocyte differentiation"
## [30] "secretion"
## [31] "positive regulation of cytokine production"
## [32] "response to unfolded protein"
## [33] "inflammatory response"
## [34] "regulation of lymphocyte activation"
## [35] "secondary metabolic process"
## [36] "regulation of cell adhesion"
## [37] "tube morphogenesis"
## [38] "cell-cell adhesion via plasma-membrane adhesion molecules"
## [39] "hemopoiesis"
## [40] "immune effector process"
The top 40 pathway with the highest
RichFactor (Count per pathway length ratio)
## [1] "double-strand break repair via break-induced replication"
## [2] "regulation of ferroptosis"
## [3] "regulation of heat generation"
## [4] "detection of molecule of bacterial origin"
## [5] "ferroptosis"
## [6] "DNA strand elongation involved in DNA replication"
## [7] "epithelial cell fate commitment"
## [8] "regulation of IRE1-mediated unfolded protein response"
## [9] "regulation of DNA-templated DNA replication initiation"
## [10] "negative regulation of endoplasmic reticulum unfolded protein response"
## [11] "heat generation"
## [12] "negative regulation of leukocyte chemotaxis"
## [13] "melanin biosynthetic process"
## [14] "melanin metabolic process"
## [15] "secondary metabolite biosynthetic process"
## [16] "integrated stress response signaling"
## [17] "regulation of T cell mediated cytotoxicity"
## [18] "secondary metabolic process"
## [19] "leukocyte activation involved in inflammatory response"
## [20] "negative regulation of chemotaxis"
## [21] "negative regulation of leukocyte migration"
## [22] "T cell mediated cytotoxicity"
## [23] "regulation of T cell mediated immunity"
## [24] "toll-like receptor signaling pathway"
## [25] "endoplasmic reticulum unfolded protein response"
## [26] "female gonad development"
## [27] "development of primary female sexual characteristics"
## [28] "response to unfolded protein"
## [29] "macrophage activation"
## [30] "positive regulation of tumor necrosis factor superfamily cytokine production"
## [31] "response to topologically incorrect protein"
## [32] "T cell mediated immunity"
## [33] "negative regulation of immune effector process"
## [34] "response to glucocorticoid"
## [35] "myeloid leukocyte activation"
## [36] "response to ketone"
## [37] "homophilic cell adhesion via plasma membrane adhesion molecules"
## [38] "response to corticosteroid"
## [39] "negative regulation of leukocyte activation"
## [40] "regulation of lymphocyte differentiation"
The top 50 most significant pathways enriched with these DEGs are
## [1] "leukocyte activation"
## [2] "response to endoplasmic reticulum stress"
## [3] "regulation of cell activation"
## [4] "cell-cell adhesion"
## [5] "multicellular organismal-level homeostasis"
## [6] "lymphocyte activation"
## [7] "regulation of leukocyte activation"
## [8] "integrated stress response signaling"
## [9] "myeloid leukocyte activation"
## [10] "response to lipid"
## [11] "response to topologically incorrect protein"
## [12] "response to unfolded protein"
## [13] "circulatory system process"
## [14] "T cell activation"
## [15] "endothelial cell migration"
## [16] "regulation of T cell mediated immunity"
## [17] "cell-cell adhesion via plasma-membrane adhesion molecules"
## [18] "regulation of leukocyte differentiation"
## [19] "positive regulation of response to external stimulus"
## [20] "toll-like receptor signaling pathway"
## [21] "response to nutrient levels"
## [22] "negative regulation of immune system process"
## [23] "homophilic cell adhesion via plasma membrane adhesion molecules"
## [24] "regulation of ferroptosis"
## [25] "endoplasmic reticulum unfolded protein response"
## [26] "regulation of T cell mediated cytotoxicity"
## [27] "leukocyte activation involved in inflammatory response"
## [28] "response to ketone"
## [29] "regulation of response to biotic stimulus"
## [30] "regulation of heat generation"
## [31] "detection of molecule of bacterial origin"
## [32] "ferroptosis"
## [33] "leukocyte differentiation"
## [34] "inflammatory response"
## [35] "positive regulation of cytokine production"
## [36] "macrophage activation"
## [37] "viral entry into host cell"
## [38] "cell junction organization"
## [39] "T cell mediated immunity"
## [40] "regulation of hemopoiesis"
## [41] "response to molecule of bacterial origin"
## [42] "regulation of IRE1-mediated unfolded protein response"
## [43] "entry into host"
## [44] "positive regulation of defense response"
## [45] "regulation of cell adhesion"
## [46] "cellular response to unfolded protein"
## [47] "blood circulation"
## [48] "blood vessel morphogenesis"
## [49] "negative regulation of cell activation"
## [50] "T cell mediated cytotoxicity"
The top 50 pathway with the highest
RichFactor (Count per pathway length ratio)
## [1] "regulation of ferroptosis"
## [2] "regulation of heat generation"
## [3] "detection of molecule of bacterial origin"
## [4] "ferroptosis"
## [5] "natural killer cell mediated cytotoxicity directed against tumor cell target"
## [6] "regulation of natural killer cell mediated immune response to tumor cell"
## [7] "anterograde dendritic transport"
## [8] "regulation of IRE1-mediated unfolded protein response"
## [9] "negative regulation of endoplasmic reticulum unfolded protein response"
## [10] "heat generation"
## [11] "positive regulation of chondrocyte differentiation"
## [12] "PERK-mediated unfolded protein response"
## [13] "IRE1-mediated unfolded protein response"
## [14] "regulation of translation in response to stress"
## [15] "regulation of cardiac muscle cell differentiation"
## [16] "integrated stress response signaling"
## [17] "negative regulation of T cell mediated immunity"
## [18] "positive regulation of cartilage development"
## [19] "regulation of endoplasmic reticulum unfolded protein response"
## [20] "regulation of T cell mediated cytotoxicity"
## [21] "leukocyte activation involved in inflammatory response"
## [22] "negative regulation of response to endoplasmic reticulum stress"
## [23] "regulation of acute inflammatory response"
## [24] "microglial cell activation"
## [25] "amino acid import across plasma membrane"
## [26] "T cell mediated cytotoxicity"
## [27] "toll-like receptor signaling pathway"
## [28] "intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress"
## [29] "endoplasmic reticulum unfolded protein response"
## [30] "regulation of T cell mediated immunity"
## [31] "response to unfolded protein"
## [32] "cellular response to unfolded protein"
## [33] "regulation of response to endoplasmic reticulum stress"
## [34] "macrophage activation"
## [35] "response to topologically incorrect protein"
## [36] "acute inflammatory response"
## [37] "T cell mediated immunity"
## [38] "cellular response to topologically incorrect protein"
## [39] "chloride transport"
## [40] "homophilic cell adhesion via plasma membrane adhesion molecules"
## [41] "regulation of myeloid leukocyte differentiation"
## [42] "response to endoplasmic reticulum stress"
## [43] "viral entry into host cell"
## [44] "entry into host"
## [45] "myeloid leukocyte activation"
## [46] "positive regulation of inflammatory response"
## [47] "monoatomic anion transport"
## [48] "response to ketone"
## [49] "negative regulation of leukocyte activation"
## [50] "endothelial cell migration"
The MSigDb is a collection of annotated gene sets. It contains 8 major collections:
## gs_collection
## gs_collection_name C1 C2 C3 C4 C5
## BioCarta Pathways 0 11088 0 0 0
## Cancer Gene Neighborhoods 0 0 0 42623 0
## Cancer Modules 0 0 0 48830 0
## Canonical Pathways 0 579 0 0 0
## Cell Type Signature 0 0 0 0 0
## Chemical and Genetic Perturbations 0 442721 0 0 0
## Curated Cancer Cell Atlas gene sets 0 0 0 7390 0
## GO Biological Process 0 0 0 0 617648
## GO Cellular Component 0 0 0 0 103371
## GO Molecular Function 0 0 0 0 113119
## GTRD 0 0 257418 0 0
## Hallmark 0 0 0 0 0
## HIPC Vaccine Response 0 0 0 0 0
## Human Phenotype Ontology 0 0 0 0 530149
## ImmuneSigDB 0 0 0 0 0
## KEGG Legacy Pathways 0 12904 0 0 0
## KEGG Medicus Pathways 0 9688 0 0 0
## MIR_Legacy 0 0 34266 0 0
## miRDB 0 0 731405 0 0
## Oncogenic Signature 0 0 0 0 0
## PID Pathways 0 8062 0 0 0
## Positional 43883 0 0 0 0
## Reactome Pathways 0 108275 0 0 0
## TFT_Legacy 0 0 155547 0 0
## WikiPathways 0 40070 0 0 0
## gs_collection
## gs_collection_name C6 C7 C8 H
## BioCarta Pathways 0 0 0 0
## Cancer Gene Neighborhoods 0 0 0 0
## Cancer Modules 0 0 0 0
## Canonical Pathways 0 0 0 0
## Cell Type Signature 0 0 157573 0
## Chemical and Genetic Perturbations 0 0 0 0
## Curated Cancer Cell Atlas gene sets 0 0 0 0
## GO Biological Process 0 0 0 0
## GO Cellular Component 0 0 0 0
## GO Molecular Function 0 0 0 0
## GTRD 0 0 0 0
## Hallmark 0 0 0 7333
## HIPC Vaccine Response 0 44668 0 0
## Human Phenotype Ontology 0 0 0 0
## ImmuneSigDB 0 948621 0 0
## KEGG Legacy Pathways 0 0 0 0
## KEGG Medicus Pathways 0 0 0 0
## MIR_Legacy 0 0 0 0
## miRDB 0 0 0 0
## Oncogenic Signature 30753 0 0 0
## PID Pathways 0 0 0 0
## Positional 0 0 0 0
## Reactome Pathways 0 0 0 0
## TFT_Legacy 0 0 0 0
## WikiPathways 0 0 0 0
Let’s see the results using different selections and using down regulated genes
## C3 : MIR_Legacy : 0 significant terms
## C3 : TFT_Legacy : 8 significant terms
## C2 : Chemical and Genetic Perturbations : 338 significant terms
## C3 : GTRD : 0 significant terms
## C8 : Cell Type Signature : 104 significant terms
## C6 : Oncogenic Signature : 19 significant terms
## C7 : HIPC Vaccine Response : 27 significant terms
## C2 : BioCarta Pathways : 0 significant terms
## C4 : Cancer Gene Neighborhoods : 0 significant terms
## C4 : Curated Cancer Cell Atlas gene sets : 7 significant terms
## C5 : GO Biological Process : 28 significant terms
## C5 : GO Cellular Component : 4 significant terms
## C7 : ImmuneSigDB : 308 significant terms
## C5 : GO Molecular Function : 1 significant terms
## H : Hallmark : 7 significant terms
## C5 : Human Phenotype Ontology : 0 significant terms
## C2 : KEGG Legacy Pathways : 0 significant terms
## C2 : KEGG Medicus Pathways : 1 significant terms
## C3 : miRDB : 0 significant terms
## C4 : Cancer Modules : 1 significant terms
## C1 : Positional : 0 significant terms
## C2 : PID Pathways : 0 significant terms
## C2 : Reactome Pathways : 9 significant terms
## C2 : Canonical Pathways : 0 significant terms
## C2 : WikiPathways : 9 significant terms
- C6 : Oncogenic signatures
- C7 : Immunologic signatures
- C5 : GO Biological Process
- C8 : Cell Type Signature
- H : Hallmark
GSEA is a functional class scoring method that evaluates whether predefined gene sets show statistically significant, concordant differences between two biological conditions. Unlike over-representation approaches, GSEA uses the entire ranked list of genes, avoiding the need for an arbitrary significance cutoff.
In this analysis, genes were ranked using a composite score based on both:
log2FoldChange)adjusted p-value,
padj)Interpretation of the ranking score
The ranking score was computed according to the following rule:
ranking <- case_when(
abs(log2FoldChange) >= 3 & padj < 0.5 ~
round((sign(log2FoldChange) * 3) * (-log10(padj) / 10), 4),
abs(log2FoldChange) < 3 & padj < 0.5 ~
round(log2FoldChange * (-log10(padj) / 10), 4),
padj >= 0.5 ~ 0
)So basically
\[Score=log2(FC)×(−log10(padj))\]
Where beyond an absolute log2FoldChange value of 3, the
ranking metric is only driven by the statistical significance
padj.
Genes with padj ≥ 0.5 are assigned a score of zero,
effectively positioning them in the middle of the ranked list.
This scoring strategy allows a balanced integration of effect size and statistical confidence, making it suitable for downstream GSEA
For this analysis we going to use gene sets from the MSigDB (as already seen above)
Below is the results represented in a dot plot
Now let’s view the enrichment plots
Below is the results represented in a dot plot
Now let’s view the enrichment plots
Below is the results represented in a dot plot
Now let’s view the enrichment plots